Visualization of the internal structure of 3-D objects on a 2-D image is an important topic within the field of computer graphics and has been applied to many industries, including medicine, geoscience, manufacturing, and drug discovery.
For example, a CT scanner can produce hundreds or even thousands of parallel 2-D image slices of a patient's body including different organs, e.g., a heart, each slice including a 2-D array of data values and each data value representing a scalar attribute of the body at a particular location, e.g., density. All the slices are stacked together to form an image volume or a volumetric dataset of the patient's body with the heart embedded therein. A 2-D image showing the 3-D structural characteristics of the heart is an important aid in the diagnosis of cardiovascular disease.
As another example, the oil industry uses seismic imaging techniques to generate a 3-D image volume of a 3-D region in the earth. Some important geological structures, such as faults or salt domes, may be embedded within the region and not necessarily on the surface of the region. Similarly, a 2-D image that fully reveals the 3-D characteristics of these structures is critical in increasing oil production.
Maximum intensity projection (MIP) ray casting is a technique developed for visualizing the interior of a solid region represented by a 3-D image volume on a 2-D image plane, e.g., a computer monitor. Typically, a plurality of rays are cast from a 2-D radiation plane into the image volume, each ray casting responsible for identifying the maximum data value (e.g., intensity) at a voxel within the image volume along its respective ray path and transferring it into an image value at a pixel on a 2-D image plane through a predefined screen transfer function. The image value is indicative of the 3-D characteristics of the objects embedded within the image volume encountered by the ray path, e.g., their shapes and orientations. The image values associated with the pixels on the 2-D image plane form an image that can be rendered on a computer monitor.
Going back to the CT example discussed above, even though a doctor can arbitrarily generate 2-D image slices of the heart by intercepting the image volume in any direction, no single image slice is able to visualize the whole surface of the heart. In contrast, a 2-D image generated through MIP ray casting of the CT image volume can easily reveal the 3-D characteristics of the heart, which is very important in many types of cardiovascular disease diagnosis. Similarly in oil exploration, MIP ray casting of 3-D seismic data can help petroleum engineers to determine more accurately the 3-D characteristics of underground geological structures of a region that are potential oil reservoirs and to increase oil production significantly.
Even though MIP ray casting plays a key role in many important fields, there are several technical challenges that need to be overcome to assure wide deployment of the MIP ray casting technique. First, MIP ray casting is a computationally expensive process. In order to produce a high quality 2-D image that can capture the 3-D characteristics of a 3-D target, MIP ray casting needs to process a large 3-D dataset, which usually means a large number of calculations. For example, it requires at least 140 million calculations to generate a 2-D image of 5122 pixels for a typical 3-D dataset of 5123 voxels using conventional MIP ray casting algorithms.
Moreover, many applications require that MIP ray casting of a 3-D dataset operate in real-time so that a user is able to view successive 2-D images of the 3-D dataset, each 2-D image having different viewing angles or visualization parameters, without a significant delay. In medical imaging, it is generally accepted that a sequential 2-D image rendering of at least six frames per second meets the need for real-time interactive feedback. This is equivalent to nearly 1 billion calculations per second.
Given the limited computational capacity of modern computers, more efficient algorithms have been developed to reduce the computational cost of MIP ray casting. However, many of these algorithms achieve their efficiency by sacrificing the quality of the generated 2-D images. For example, a common problem with discrete representation of a continuous object is the jitter effect, which is most obvious when a user zooms in to view more details of the continuous object. If the jitter effect is not carefully controlled, it may significantly corrupt the quality of an image generated by a MIP ray casting algorithm.
Therefore, it would be desirable to develop a new MIP ray casting method and system that increase the rendering efficiency while having less or preferably imperceptible impact on the image quality.